SPLODER (Smart Planning and Operation of Distributed Energy Resources (DER)) was developed at IIT (Institute for Research in Technology).
The SPLODER model, which is referred to in the literature in its System version, is a planning tool for both centralized and distributed generation and storage resources of the electric power system. This version has been developed for regulatory analysis and to support the strategic decisions of electric utilities. It includes a version formerly known as Building, which allows detailed consumption modeling.
By means of input data and an optimization problem, the model is able to provide the optimal mix of technologies in economic terms to meet the demand, both in terms of energy consumed and in terms of guarantee or firmness of supply. Therefore, the objective function of the model minimizes the cost of investments in new centralized and distributed resources, as well as the operating cost of both these new investments and existing resources.
To be computationally efficient, a generation and storage expansion model such as SPLODER needs to simplify the representation of system operation. That is why the model simplifies the system operation by considering 4 representative weeks of the year with hourly granularity. Each representative week has a different weight according to the number of weeks it represents:
In order to adequately reflect the loading and unloading cycles of the storage facilities and the management of hydraulic production, the technologies that allow some type of storage comply with the fact that the load level is the same in the first and last hour of the representative week. Except for the case of pumping, which is allowed to store water from one week to another because otherwise its potential would not be well collected.
To mitigate the effect of working with four representative weeks of the year and to better evaluate the contribution to the firmness of supply of the different technologies, the SPLODER model incorporates an explicit restriction that establishes as a requirement to cover the firmness needs of the system, using predetermined coefficients of contribution to the firmness of each of the competing technologies. The competitiveness of the technologies may depend significantly (together with their other characteristics of CAPEX, OPEX, performance, storage capacity, etc.) on the firmness contribution coefficient attributed to them.
The input parameters for each of the case studies include:
Once the model has been executed, the following variables are obtained as results:
The model also allows imposing other restrictions that are very useful when designing scenarios:
The model has three price signals. First the market price that it estimates with the selected mix, then it considers a retribution to the technologies for their contribution to the system with firm capacity in case it requires them only for this reason and not for cost competitiveness, and finally an additional payment to the renewable energy sources in case it is necessary to reach a certain established percentage of renewables. The tariff design of the model consists of:
 Francisco Martín Martínez, Álvaro Sánchez Miralles, Michel Rivier Abad, Modeling tools for planning and operation of DERs and their impact in microgrids and centralized sources, 16 (2017). https://doi.org/10.32964/tj16.5.
 F. Martín-Martínez, A. Sánchez-Miralles, M. Rivier, C.F. Calvillo, Centralized vs distributed generation. A model to assess the relevance of some thermal and electric factors. Application to the Spanish case study, Energy. 134 (2017) 850–863.
 T. Gerres, J.P.C. Ávila, F.M. Martínez, M.R. Abbad, R.C. Arín, Á.S. Miralles, T.G. San Román, “Rethinking the electricity market design: Remuneration mechanisms to reach high RES shares. Results from a Spanish case study” (Energy Policy (2019) 129 (1320–1330), (S0301421519301995), (10.1016/j.enpol.2019.03.034)), Energy Policy. 131 (2019) 434. https://doi.org/10.1016/j.enpol.2019.04.014.
[4[ T. Freire-Barceló, F. Martín-Martínez, Á. Sánchez-Miralles, M. Rivier, T.G.S. Román, S. Huclin, J.P.C. Ávila, A. Ramos, Storage and demand response contribution to firm capacity: analysis of the Spanish electricity system, Energy Reports. 8 (2022) 10546–10560. https://doi.org/10.1016/j.egyr.2022.08.014.
 M. Rivier, F. Martín, T. Freire-Barceló, Solar and wind production profiles and 2030 disaggregated electricity demand profiles to feed the SPLODER generation expansion planning model. Project: FLEXENER. Funded by Siemens Gamesa Renewable Energy Innovation & Technology S.L. Dec/2022.